江苏一次大暴雨过程的诊断与中尺度分析

A diagnose and mesoscale analysis of a torrential rain process in Jiangsu province

利用FY-2E卫星TBB资料、加密自动气象站资料、多普勒雷达产品、NECP格点资料,对2010年7月12—13日发生在江苏沿江地区的一次大暴雨过程进行了诊断和中尺度分析。结果表明:此次大暴雨过程是在有利的大尺度环流形势稳定维持下发生的,强度大、中尺度特征非常明显;高空辐散低空辐合的有利配置和垂直螺旋度是此次暴雨过程的动力机制;非地转风和垂直环流圈的上升支气流触发了不稳定能量释放,激发了暴雨过程;正反环流圈不断地加强和补充暴雨所需的水汽和能量,使暴雨得以维持;10个中尺度雨团的频繁活动直接造成了此次暴雨。其中,起重要作用的是合并而成的A、B雨团,雨团的合并使雨区扩大、雨势更强、生命史更长;3个β-MCS是造成此次暴雨的中尺度对流系统,与雨团发展直接相联系的是≤-62℃,甚至≤-72℃的冷云盖。1号β-MCS影响时段内,由多个对流单体回波合并形成的块状对流回波。3号β-MCS影响时段内,对流单体回波合并后形成一条东西向带状回波,在径向速度场上则出现了逆风区特征。地面中尺度辐合系统与雨团的发生发展关系十分密切,且对雨团的发生发展具有一定的预示作用。

Based on black body temperature data of FY-2E meteorological satellite,Doppler radar images and NCEP reanalysis data,combining the intensive observation of automatic weather station data,a diagnose and mesoscale analysis of the torrential rain along Yangtze river basin in Jiangsu province on July 12-13,2010 was performed.The results show that the heavy rainfall occurred in the favorable and stable environmental conditions and had the feature of obvious mesoscale.Convergence at the low-level,divergence at the upper level and vertical helicity provided dynamic mechanism for the rainstorm process.Upward flow of vertical cell of ageostrophic wind and vertical velocity could trigger the instable energy to be released.A frequent activity of ten rain clusters directly brought about the torrential rain.And the mergered rain clusters called A and B played an important role in heavy rainfall process,and which led to larger rain area coverage,stronger rain intensity and longer life span.Three β-MCS continued to occur in Jiangsu one after another during the course of rainstorm.It deserves extra attention that the development of rain clusters was directly associated with central cold cover in which the temperature fell below-62 ℃,even lower than-72 ℃.The process of heavy rainfall appeared in the area of adverse wind and of the echo clusters merging.The surface mesoscale convergence system had close links with rain clusters and had good indication for rain clusters forecast.